import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_snake_case								=    logging.getLogger(__name__)




def 	_UpperCamelCase (      )    -> Dict:
							__UpperCAmelCase    :      Any       = argparse.ArgumentParser(
							    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."				)
							parser.add_argument("--file_path",     type=_lowerCamelCase,     default="data/dump.txt",     help="The path to the data."				)
							parser.add_argument("--tokenizer_type",     type=_lowerCamelCase,     default="bert",     choices=["bert", "roberta", "gpt2"]				)
							parser.add_argument("--tokenizer_name",     type=_lowerCamelCase,     default="bert-base-uncased",     help="The tokenizer to use."				)
							parser.add_argument("--dump_file",     type=_lowerCamelCase,     default="data/dump",     help="The dump file prefix."				)
							__UpperCAmelCase    :      Tuple       = parser.parse_args()

							logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''				)
							if args.tokenizer_type == "bert":
														__UpperCAmelCase    :      Any       = BertTokenizer.from_pretrained(args.tokenizer_name				)
														__UpperCAmelCase    :      Tuple       = tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
														__UpperCAmelCase    :      str       = tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
							elif args.tokenizer_type == "roberta":
														__UpperCAmelCase    :      int       = RobertaTokenizer.from_pretrained(args.tokenizer_name				)
														__UpperCAmelCase    :      Optional[Any]       = tokenizer.special_tokens_map["cls_token"]  # `<s>`
														__UpperCAmelCase    :      Optional[Any]       = tokenizer.special_tokens_map["sep_token"]  # `</s>`
							elif args.tokenizer_type == "gpt2":
														__UpperCAmelCase    :      List[str]       = GPTaTokenizer.from_pretrained(args.tokenizer_name				)
														__UpperCAmelCase    :      List[Any]       = tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
														__UpperCAmelCase    :      Any       = tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

							logger.info(f'''Loading text from {args.file_path}'''				)
							with open(args.file_path,     "r",     encoding="utf8"				) as fp:
														__UpperCAmelCase    :      List[str]       = fp.readlines()

							logger.info("Start encoding"				)
							logger.info(f'''{len(_lowerCamelCase				)} examples to process.'''				)

							__UpperCAmelCase    :      int       = []
							__UpperCAmelCase    :      Dict       = 0
							__UpperCAmelCase    :      Optional[int]       = 1_0000
							__UpperCAmelCase    :      List[str]       = time.time()
							for text in data:
														__UpperCAmelCase    :      int       = f'''{bos} {text.strip()} {sep}'''
														__UpperCAmelCase    :      Dict       = tokenizer.encode(_lowerCamelCase,     add_special_tokens=_lowerCamelCase				)
														rslt.append(_lowerCamelCase				)

														iter += 1
														if iter % interval == 0:
																					__UpperCAmelCase    :      List[Any]       = time.time()
																					logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''				)
																					__UpperCAmelCase    :      Dict       = time.time()
							logger.info("Finished binarization"				)
							logger.info(f'''{len(_lowerCamelCase				)} examples processed.'''				)

							__UpperCAmelCase    :      str       = f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
							__UpperCAmelCase    :      Optional[Any]       = tokenizer.vocab_size
							if vocab_size < (1 << 16):
														__UpperCAmelCase    :      List[Any]       = [np.uintaa(_lowerCamelCase				) for d in rslt]
							else:
														__UpperCAmelCase    :      Optional[int]       = [np.intaa(_lowerCamelCase				) for d in rslt]
							random.shuffle(rslt_				)
							logger.info(f'''Dump to {dp_file}'''				)
							with open(_lowerCamelCase,     "wb"				) as handle:
														pickle.dump(rslt_,     _lowerCamelCase,     protocol=pickle.HIGHEST_PROTOCOL				)


if __name__ == "__main__":
	main()












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[str]          =		{}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							str          =		['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Any          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




